CN113033680B - Video classification method and device, readable medium and electronic equipment - Google Patents
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Abstract
The disclosure relates to a video classification method, a device, a readable medium and an electronic apparatus, wherein the method comprises the following steps: acquiring a classification label of a target video as a first label; under the condition that the playing times are higher than a first preset playing amount threshold value, determining a predicted tag of the target video as a second tag through a pre-trained first video classification model; and determining the classified label of the target video as the second label under the condition that the second label is not the same label as the first label. Therefore, after the playing times are higher than the first preset playing amount threshold, the target video can be subjected to video classification label re-prediction in a mode different from the mode of determining the first label, so that a second label of the target video is obtained, and the video classification label to which the target video belongs is corrected according to the second label, so that the accuracy of the classification label of the target video with higher playing times can be ensured, and the accuracy of the classification label of the target video with higher playing times is improved.
Description
Technical Field
The disclosure relates to the technical field of videos, and in particular relates to a video classification method, a device, a readable medium and electronic equipment.
Background
In the prior art, when the videos in each platform are classified by labels, only one classification mode, such as a machine learning model trained in advance, is usually adopted, however, the videos in the current video platform are too many in variety and too complicated in content, and only the machine learning model trained in advance is adopted, so that good classification effects on various types of videos are difficult to achieve.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In a first aspect, the present disclosure provides a video classification method, the method comprising:
acquiring a classification label of a target video as a first label;
Under the condition that the playing times are higher than a first preset playing amount threshold value, determining a prediction tag of the target video as a second tag through a pre-trained first video classification model;
And determining the classification label of the target video as the second label under the condition that the second label is not the same label as the first label.
In a second aspect, the present disclosure provides a video classification apparatus, the apparatus comprising:
the acquisition module is used for acquiring the classification label of the target video as a first label;
The determining module is used for determining a prediction tag of the target video as a second tag through a pre-trained first video classification model under the condition that the playing times are higher than a first preset playing amount threshold value;
and the correction module is used for determining the classification label of the target video as the second label when the second label is not the same label as the first label.
In a third aspect, the present disclosure provides a computer readable medium having stored thereon a computer program which when executed by a processing device implements the steps of the method described in the first aspect.
In a fourth aspect, the present disclosure provides an electronic device comprising:
a storage device having a computer program stored thereon;
Processing means for executing said computer program in said storage means to carry out the steps of the method described in the first aspect.
Through the technical scheme, the video classification labels of videos without playing frequency information or videos with fewer playing frequency can be determined through any video classification mode, such as the mode of the second video classification model, so that any video can be ensured to obtain the corresponding video label as the first label; and after the playing times are higher than the first preset playing amount threshold, the target video can be subjected to video classification label re-prediction in a mode different from that of determining the first label, for example, the first video classification model is used for determining, so that a second label of the target video is obtained, and the video classification label to which the target video belongs is corrected according to the second label, so that the accuracy of the classification label of the target video with higher playing times can be ensured.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale. In the drawings:
fig. 1 is a flowchart illustrating a video classification method according to an exemplary embodiment of the present disclosure.
Fig. 2 is a flowchart illustrating a video classification method according to still another exemplary embodiment of the present disclosure.
Fig. 3 is a flow chart of a method of training the first video classification model in a video classification method according to yet another exemplary embodiment of the disclosure.
Fig. 4 is a block diagram illustrating a video classification apparatus according to an exemplary embodiment of the present disclosure.
Fig. 5 shows a schematic structural diagram of an electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
Fig. 1 is a flowchart illustrating a video classification method according to an exemplary embodiment of the present disclosure. As shown in fig. 1, the method includes steps 101 to 103.
In step 101, a classification tag of a target video is acquired as a first tag. The target video may be any form of video that requires tag classification. For example, the video may be a short video published by a user in a short video platform, or a long video published by a user in another video platform.
In step 102, if the number of play times is higher than a first preset play amount threshold, determining, by using a first video classification model trained in advance, a prediction tag of the target video as a second tag.
The number of plays can be determined according to the number of times recording rules of different video platforms, and the specific method for determining the number of plays is not limited in the present application.
And under the condition that the playing times are higher than the first preset playing amount threshold, the playing times of the target video can be represented to be higher, namely, the first preset playing amount threshold can be used for screening out the target video with higher playing times as the video for predicting the second label.
The first video classification model used to determine the second tag may be any video classification model as long as a predictive tag of the target video is available.
In step 103, in the case that the second tag is not the same tag as the first tag, determining the classification tag of the target video as the second tag.
And carrying out secondary label prediction on the target video with higher playing times, thereby improving the label accuracy of the target video with higher playing times.
The class label as the first label of the target video may be a class label determined by any means. For example, the method shown in fig. 2 may be used.
Fig. 2 is a flowchart illustrating a video classification method according to still another exemplary embodiment of the present disclosure. As shown in fig. 2, the method further comprises step 201 and step 202.
In step 201, determining the classification tag of the target video by a second video classification model;
in step 202, the classification label of the target video is taken as a first label.
The second video classification model and the first video classification model are different video classification models. The video classification model may be different video classification models obtained by training different training samples of the model, different video classification models obtained by adopting different video classification methods, or different video classification models obtained by adopting different neural networks, so long as the video classification model is not the same as the first video classification model.
In a possible implementation manner, the training data of the second video classification model may be sample video with play times and/or without play times. That is, the training data of the second video classification model is random annotation data.
Through the technical scheme, the video classification labels of videos without playing frequency information or videos with fewer playing frequency can be determined through any video classification mode, such as the mode of the second video classification model, so that any video can be ensured to obtain the corresponding video label as the first label; and after the playing times are higher than the first preset playing amount threshold, the target video can be subjected to video classification label re-prediction in a mode different from that of determining the first label, for example, the first video classification model is used for determining, so that a second label of the target video is obtained, and the video classification label to which the target video belongs is corrected according to the second label, so that the accuracy of the classification label of the target video with higher playing times can be ensured.
In another possible implementation manner, the training data of the first video classification model is a sample video with a playing frequency higher than a second preset playing amount threshold. That is, when training the first video classification model, all training video data used is a high play amount sample video. Thus, since the effect of the machine learning model is affected by the data distribution, and the learning of the machine learning model can only be performed on a certain fixed distribution, the machine learning model works best on a distribution similar to the training set. Therefore, the first video classification model can have better label prediction effect on the target video with the playing times higher than the second preset playing amount threshold value through limiting the playing times distribution of the training data of the first video classification model.
For example, the applicant finds that in most short video platforms, if a plurality of short videos with fixed time length are uploaded and then are in different playing times, the distribution in the video content of the short videos has larger difference, for example, in videos with lower playing times, the content of the video is single, the information amount is not high, and in videos with higher playing times, the content proportion of scenario deductions, knowledge science popularization and the like with higher information content is obviously improved. There is some difference in video content between different play times distributions. Therefore, if the training data for training the first video classification model is limited by the threshold according to the playing times, the classification prediction performance of the first video classification model obtained by training in the target video in the distribution of which the playing times are higher than the first preset playing amount threshold is better, and the accuracy is higher.
The first preset play amount threshold and the second preset play amount threshold may be set to be completely identical or two similar different thresholds according to practical application, so long as the label classification prediction effect of the first video classification model on the target video higher than the first preset play amount threshold can be achieved, and the label classification effect is better than the classification effect of the first label for predicting the target video. For example, the second preset play amount threshold may be set relatively lower than the first preset play amount threshold, so long as the play times higher than the first preset play amount threshold are included in the play times higher than the second preset play amount threshold, and the specific value is not limited in the disclosure.
Fig. 3 is a flow chart of a method of training the first video classification model in a video classification method according to yet another exemplary embodiment of the disclosure. As shown in fig. 3, the method comprises steps 301 to 303.
In step 301, a sample video with the number of plays higher than the second preset play amount threshold is obtained. The number of plays may be determined according to the play count recording rules for different video platforms.
In step 302, the sample video is labeled with a class label. The mode of classifying and labeling the sample video can be manual labeling. In the case where the first tag of the target video is determined by the second video classification model, for example, the classification labels of the sample videos in the training data of the second video classification model may be manually labeled.
In step 303, the first video classification model is model trained by the sample video with the classification label labels.
Fig. 4 is a block diagram illustrating a video classification apparatus according to an exemplary embodiment of the present disclosure. As shown in fig. 4, the apparatus includes: an acquiring module 10, configured to acquire a classification tag of a target video as a first tag; the determining module 20 is configured to determine, when the number of play times is higher than a first preset play amount threshold, a predicted tag of the target video as a second tag through a first video classification model that is trained in advance; and the correction module 30 is configured to determine the classification label of the target video as the second label if the second label is not the same label as the first label.
Through the technical scheme, the video classification labels of videos without playing frequency information or videos with fewer playing frequency can be determined through any video classification mode, such as the mode of the second video classification model, so that any video can be ensured to obtain the corresponding video label as the first label; and after the playing times are higher than the first preset playing amount threshold, the target video can be subjected to video classification label re-prediction in a mode different from that of determining the first label, for example, the first video classification model is used for determining, so that a second label of the target video is obtained, and the video classification label to which the target video belongs is corrected according to the second label, so that the accuracy of the classification label of the target video with higher playing times can be ensured.
In one possible implementation manner, the training data of the first video classification model is a sample video with a playing frequency higher than a second preset playing amount threshold.
In one possible implementation, the first video classification model is trained by: acquiring the sample video with the playing times higher than the second preset playing quantity threshold value; performing classification label labeling on the sample video; and carrying out model training on the first video classification model through the sample video with the classification label labels.
In one possible implementation, the acquisition module 10 includes: the first acquisition sub-module is used for determining the classification label of the target video through a second video classification model; and the second acquisition sub-module is used for taking the classification label of the target video as a first label.
In a possible implementation manner, the training data of the second video classification model is a sample video with play times and/or no play times.
Referring now to fig. 5, a schematic diagram of an electronic device 500 suitable for use in implementing embodiments of the present disclosure is shown. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 5 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 5, the electronic device 500 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 501, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
In general, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 507 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 508 including, for example, magnetic tape, hard disk, etc.; and communication means 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 shows an electronic device 500 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or from the storage means 508, or from the ROM 502. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 501.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some embodiments, communications may be made using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a classification label of a target video as a first label; under the condition that the playing times are higher than a first preset playing amount threshold value, determining a prediction tag of the target video as a second tag through a pre-trained first video classification model; and determining the classification label of the target video as the second label under the condition that the second label is not the same label as the first label.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented in software or hardware. The name of the module is not limited to the module itself in some cases, and for example, the acquisition module may also be described as "a module that acquires a classification tag of the target video as the first tag".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In accordance with one or more embodiments of the present disclosure, example 1 provides a video classification method, the method comprising: acquiring a classification label of a target video as a first label; under the condition that the playing times are higher than a first preset playing amount threshold value, determining a prediction tag of the target video as a second tag through a pre-trained first video classification model; and determining the classification label of the target video as the second label under the condition that the second label is not the same label as the first label.
In accordance with one or more embodiments of the present disclosure, example 2 provides the method of example 1, wherein the training data of the first video classification model is a sample video having a number of plays above a second preset play amount threshold.
In accordance with one or more embodiments of the present disclosure, example 3 provides the method of example 2, the first video classification model is trained by: acquiring the sample video with the playing times higher than the second preset playing quantity threshold value; performing classification label labeling on the sample video; and carrying out model training on the first video classification model through the sample video with the classification label labels.
According to one or more embodiments of the present disclosure, example 4 provides the method of example 1, the obtaining a classification tag of the target video as the first tag comprising: determining the classification labels of the target videos through a second video classification model; and taking the classification label of the target video as a first label.
Example 5 provides the method of example 4, the training data of the second video classification model being sample video with and/or without play times, according to one or more embodiments of the present disclosure.
Example 6 provides a video classification apparatus according to one or more embodiments of the present disclosure, the apparatus comprising: the acquisition module is used for acquiring the classification label of the target video as a first label; the determining module is used for determining a prediction tag of the target video as a second tag through a pre-trained first video classification model under the condition that the playing times are higher than a first preset playing amount threshold value; and the correction module is used for determining the classification label of the target video as the second label when the second label is not the same label as the first label.
In accordance with one or more embodiments of the present disclosure, example 7 provides the apparatus of example 6, wherein the training data of the first video classification model is a sample video having a number of plays above a second preset play amount threshold.
In accordance with one or more embodiments of the present disclosure, example 8 provides the apparatus of example 7, the first video classification model is trained by: acquiring the sample video with the playing times higher than the second preset playing quantity threshold value; performing classification label labeling on the sample video; and carrying out model training on the first video classification model through the sample video with the classification label labels.
According to one or more embodiments of the present disclosure, example 9 provides a computer-readable medium having stored thereon a computer program which, when executed by a processing device, implements the steps of the method of any of examples 1-5.
In accordance with one or more embodiments of the present disclosure, example 10 provides an electronic device, comprising: a storage device having a computer program stored thereon; processing means for executing the computer program in the storage means to implement the steps of the method of any one of examples 1-5.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims. The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Claims (9)
1. A method of video classification, the method comprising:
acquiring a classification label of a target video as a first label;
under the condition that the playing times of the target video are higher than a first preset playing amount threshold value, determining a prediction tag of the target video as a second tag through a pre-trained first video classification model;
Determining a classification label of the target video as the second label under the condition that the second label is not the same label as the first label;
the step of obtaining the classification label of the target video as a first label comprises the following steps:
determining the classification labels of the target videos through a second video classification model;
And taking the classification label of the target video as a first label.
2. The method of claim 1, wherein the training data of the first video classification model is sample video having a number of plays greater than a second predetermined play amount threshold.
3. The method of claim 2, wherein the first video classification model is trained by:
Acquiring the sample video with the playing times higher than the second preset playing quantity threshold value;
performing classification label labeling on the sample video;
And carrying out model training on the first video classification model through the sample video with the classification label labels.
4. The method of claim 1, wherein the training data of the second video classification model is a sample video with and/or without play times.
5. A video classification device, the device comprising:
the acquisition module is used for acquiring the classification label of the target video as a first label;
The determining module is used for determining a prediction tag of the target video as a second tag through a pre-trained first video classification model under the condition that the playing times of the target video are higher than a first preset playing amount threshold value;
The correction module is used for determining the classification label of the target video as the second label when the second label is not the same label as the first label;
wherein, the acquisition module includes: the first acquisition sub-module is used for determining the classification label of the target video through a second video classification model; and the second acquisition sub-module is used for taking the classification label of the target video as a first label.
6. The apparatus of claim 5, wherein the training data of the first video classification model is sample video that is played more times than a second preset play amount threshold.
7. The apparatus of claim 6, wherein the first video classification model is trained by:
Acquiring the sample video with the playing times higher than the second preset playing quantity threshold value;
performing classification label labeling on the sample video;
And carrying out model training on the first video classification model through the sample video with the classification label labels.
8. A computer readable medium on which a computer program is stored, characterized in that the program, when being executed by a processing device, carries out the steps of the method according to any one of claims 1-4.
9. An electronic device, comprising:
a storage device having a computer program stored thereon;
processing means for executing said computer program in said storage means to carry out the steps of the method according to any one of claims 1-4.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106055617A (en) * | 2016-05-26 | 2016-10-26 | 乐视控股（北京）有限公司 | Data pushing method and device |
| CN108259672A (en) * | 2018-01-30 | 2018-07-06 | 努比亚技术有限公司 | A kind of processing method of multimedia content, terminal and computer storage media |
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| CN109088987A (en) * | 2018-07-26 | 2018-12-25 | 阿里巴巴集团控股有限公司 | A kind of audio and video playing method, apparatus and electronic equipment |
| CN109361929B (en) * | 2018-09-28 | 2021-05-28 | 武汉斗鱼网络科技有限公司 | Method for determining live broadcast room label and related equipment |
| CN110113638A (en) * | 2019-05-10 | 2019-08-09 | 北京奇艺世纪科技有限公司 | A kind of prediction technique, device and electronic equipment |
| CN112261226B (en) * | 2020-10-21 | 2022-09-16 | 北京字跳网络技术有限公司 | Horizontal screen interaction method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106055617A (en) * | 2016-05-26 | 2016-10-26 | 乐视控股（北京）有限公司 | Data pushing method and device |
| CN108259672A (en) * | 2018-01-30 | 2018-07-06 | 努比亚技术有限公司 | A kind of processing method of multimedia content, terminal and computer storage media |
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